Here’s an interesting look at Microsoft’s AI strategy from Computer Weekly, with some great quotes from Scott Guthrie.

For AI, Microsoft appears to be putting a lot of emphasis on the developer community. Its head of Cloud + AI business is Scott Guthrie, who has a developer background and co-invented Microsoft’s original ASP.NET web application technology. Guthrie is the Microsoft executive vice president with responsibility for a large chunk of Microsoft’s business, including Cloud, Windows Server, database, CRM, ERP, and the AI platform.

“Our business model at Microsoft is focused on delivering a B2B [business to business] experience and helping our partners and customers deliver to their customers, and that’s a differentiator that increasingly comes up versus Amazon as well as Google, given that their business model is ultimately about reaching users.”

Here’s another round up post from the recent TensorFlow Summit — this one focusing on the product announcements.

TensorFlow has been unrolled as the open-source app development platform that allows individual developers and development teams to build and train models through Machine Learning and utilise the same in their app development projects. Google for the last two years is already holding developer summit for this platform and in the latest and third annual TensorFlow Developer Summit, the company made a few significant announcements including the release of TensorFlow 2.0. version. Let us briefly take a look at the different releases at the summit.

If 2018 was the year of AI & ML, then 2019 is going to be the year of AI/ML Operationalization. I see this all the time with customers: AI requires a lot of teams to work together that traditionally have not worked together. 

Here’s an interesting article on the five things that all great companies do successfully adopt AI. Listed last, but certainly not least, in the list is having a Data Driven culture. Aside from the fact that it subliminally promotes my podcast, the importance of company culture cannot be overstated.

From the article (emphasis added):

Data-driven Culture Without a strong, data-driven organizational culture, none of the above can ever be successful. Some of the world’s largest companies like Amazon, Google, and Facebook have embraced data as part of their organization’s culture. Here are some things they, and the aforementioned customers, do well:

  • Treating data as an enterprise asset.
  • Creating a central data strategy (a Data Hub) to integrate all types of data
  • Strong data governance & data lineage.
  • ML-enabled data cataloging to find data efficiently.
  • Robust Master/Reference data management.
  • Mixing IT-led and self-service data preparation & wrangling capabilities.
  • Implementing self-service exploration capabilities to visually interact with data.

Data is an asset, potentially a very lucrative one and likely one that will either drive your business into the next decade or drive you out of business before the next decade. 

In this video from a recent talk at MIT, Demis Hassabis discusses the capabilities and power of self-learning systems. He illustrates this with reference to some of DeepMind’s recent breakthroughs, and talks about the implications of cutting-edge AI research for scientific and philosophical discovery.

What’s more impressive, is Demis’ biography. From the description:

Speaker Biography: Demis is a former child chess prodigy, who finished his A-levels two years early before coding the multi-million selling simulation game Theme Park aged 17. Following graduation from Cambridge University with a Double First in Computer Science he founded the pioneering video games company Elixir Studios producing award winning games for global publishers such as Vivendi Universal. After a decade of experience leading successful technology startups, Demis returned to academia to complete a PhD in cognitive neuroscience at UCL, followed by postdocs at MIT and Harvard, before founding DeepMind. His research into the neural mechanisms underlying imagination and planning was listed in the top ten scientific breakthroughs of 2007 by the journal Science. Demis is a 5-times World Games Champion, a Fellow of the Royal Society of Arts, and the recipient of the Royal Society’s Mullard Award and the Royal Academy of Engineering’s Silver Medal.

Which Face Is Real? was developed by Jevin West and Carl Bergstrom from the University of Washingtion as part of the Calling Bullshit Project.

It acts as a kind of game that anyone can play. Visitors to the site have a choice of two images, one of which is real and the other of which is a fake generated by StyleGAN.

As to what motivated them, here’s a quote from the article:

Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences.

Which Face is Real?

Read more www.kdnuggets.com

InfoWorld talks about the power of Microsoft’s new Cognitive Service: anomaly detection.

Fortunately, the first new cognitive service to explore other aspects of machine learning entered beta recently: adding anomaly detection to the roster. Anomaly detection is an important AI tool, analyzing time-series data for items that are outside normal operating characteristics for the data source. That makes it an extremely flexible tool because modern businesses have a lot of streamed data, from financial transactions to software logs to device telemetry. The ability to use one API to work across all these different feeds shouldn’t be underestimated, because it makes building appropriate software a lot easier.

Here’s an interesting article on creating and using custom loss functions in Keras. Why would you need to do this?

Here’s one example from the article:

Let’s say you are designing a Variational Autoencoder. You want your model to be able to reconstruct its inputs from the encoded latent space. However, you also want your encoding in the latent space to be (approximately) normally distributed.

Read more www.kdnuggets.com

Chances are that you already know what TensforFlow is and why it’s important. However, as AI spreads from the lab to data science departments and into production, tools like TensorFlow will start crossing paths with the rest of enterprise IT.

TechRebublic has details on a free ebook on TensorFlow: a Guide for IT Pros.

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It offers tremendous opportunities for developers building machine learning into their products. This ebook looks at what TensorFlow is, where it’s headed, and how it’s being put to work.